Kubernetes Cost Optimization: 7 Ways to Cut Your Bill in 2026

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Table of Contents

• What Is Kubernetes Cost Optimization?
• How Do I Right-Size Kubernetes Resources?
• When Should I Use Kubernetes Autoscaling?
• EKS vs GKE vs AKS: Which Costs Less in 2026?
• How Do I Eliminate Idle Kubernetes Resources?
• What Are the Best Kubernetes Cost Tools in 2026?
• How Do I Use Spot Instances for Kubernetes?
• FAQ
• Sources

TLDR: Most K8s clusters run at 30-35% CPU utilization while you pay for 100%. Right-size pods to p95 usage, implement autoscaling with Karpenter, and run fault-tolerant workloads on spot instances. These three levers typically cut cluster spend by 40-60% without hurting performance.

Your Kubernetes bill is probably 40-60% higher than it needs to be—and that’s not a guess.

As a cloud architect who’s managed $2M+ in AWS infrastructure across 50+ production clusters, I’ve seen teams routinely waste thousands monthly on overprovisioned nodes, idle capacity, and misconfigured autoscaling. The average K8s cluster runs at 30-35% CPU utilization while you’re paying for 100%.

Kubernetes cost optimization in 2026 centers on three core levers: right-sizing resources based on actual usage (not fear-based overprovisioning), implementing intelligent autoscaling that scales down during low demand, and leveraging spot instances for fault-tolerant workloads. Combined with proper cost visibility, these strategies typically reduce cluster spend by 40-60% without touching application performance.

What Is Kubernetes Cost Optimization?

Kubernetes cost optimization is the practice of reducing infrastructure spend while maintaining (or improving) application performance through right-sizing, autoscaling, and strategic use of cloud pricing models. Unlike traditional VMs where you can easily see what’s running, K8s abstracts resources across nodes and pods—making it trivially easy to burn money on unused capacity.

The core problem isn’t Kubernetes itself. It’s that most teams set resource requests based on worst-case scenarios (because nobody wants to get paged at 2am), then never revisit them. You end up with pods requesting 4 CPU cores that use 0.5 cores at p95.

I’ve seen production clusters where removing CPU limits and rightsizing based on p95 usage cut compute costs by 45% in three weeks. The application ran better because pods weren’t getting throttled unnecessarily.

How Do I Right-Size Kubernetes Resources?

Right-sizing means setting CPU and memory requests/limits based on actual usage patterns (typically p95 or p99) rather than guesswork. Start with high-spend workloads, analyze real resource consumption over 2-4 weeks, then adjust requests to match observed usage plus 15-20% headroom. Remove CPU limits to improve bin-packing efficiency.

Here’s the two-pronged approach that actually works:

Node-level optimization: Choose instance types that match your workload profile. If you’re running memory-intensive workloads, AWS r7i instances beat general-purpose m7i on price-per-GB. For GKE, n2-highmem instances are 15-20% cheaper than n2-standard for RAM-heavy pods.

Pod-level optimization: Use a tool like PerfectScale or Kubecost to analyze actual resource usage. Look for pods with CPU requests of 2000m that peak at 400m. Those are your low-hanging fruit.

Right-sizing priority order:

The mistake teams make is rightsizing everything at once. Start with the top 10, validate, then move down the list.

When Should I Use Kubernetes Autoscaling?

You should implement autoscaling when your workload has predictable traffic patterns or bursty demand—basically any production application that isn’t running at constant 80%+ utilization 24/7. Pair Horizontal Pod Autoscaler (HPA) with Cluster Autoscaler or Karpenter so nodes scale down when pods scale down, otherwise you’re just shuffling pods around expensive idle nodes.

Kubernetes offers three autoscaling mechanisms:

Horizontal Pod Autoscaler (HPA): Adjusts the number of pod replicas based on CPU, memory, or custom metrics. This is your primary scaling tool for production workloads. HPA checks metrics every 15 seconds by default and scales pods up or down to maintain target utilization.

Cluster Autoscaler (CA): Adds or removes nodes when pods can’t schedule due to insufficient capacity, or when nodes are underutilized (below 50% for 10+ minutes). It checks every 10 seconds for pending pods. The catch—CA relies on accurate pod resource requests, so if those are wrong, your autoscaling will be wrong.

Vertical Pod Autoscaler (VPA): Adjusts CPU and memory requests/limits automatically. Don’t use this in production alongside HPA—they conflict. VPA works best for batch jobs or workloads where you can tolerate pod restarts.

The real cost savings comes from pairing HPA with cluster-level autoscaling. I’ve seen teams implement HPA, celebrate when pods scale down during off-peak hours, then realize they’re still paying for the same number of nodes because CA isn’t configured properly.

For AWS EKS, Karpenter is better than Cluster Autoscaler—it provisions nodes 3-5x faster and bins pods more efficiently. If you’re on EKS 1.28+, use Karpenter instead of CA.

EKS vs GKE vs AKS: Which Costs Less in 2026?

For most workloads, Azure AKS is the cheapest ($0 control plane fee + competitive node pricing), followed by GKE (strong for AI/ML with TPU discounts), while AWS EKS is typically 15-25% more expensive due to $73/month control plane fees and higher networking costs. The gap narrows at 500+ nodes where EKS volume pricing kicks in, and for teams already committed to AWS services like RDS or Lambda.

Here’s the breakdown (as of January 2026):

Bottom line for different scenarios:

• Small-medium workloads (<100 nodes): AKS wins on control plane savings
• AI/ML training: GKE is 18-22% cheaper with TPU v5 instances
• Enterprise (500+ nodes): EKS pricing becomes competitive, especially if you’re already all-in on AWS
• Data-intensive apps: GKE’s $0.04/GB storage is hard to beat

The real cost isn’t just compute. Factor in egress—if you’re serving traffic globally, AWS CloudFront + EKS integration can offset EKS’s higher baseline cost. For GKE, intra-region traffic gets significant discounts.

I’ve migrated teams from EKS to GKE and saved 30% on compute alone, but then they spent two months rebuilding CI/CD integrations with AWS services. The 30% savings evaporated in engineering time. Choose based on your ecosystem lock-in, not just the sticker price.

How Do I Eliminate Idle Kubernetes Resources?

Set up namespace-level resource quotas, implement pod disruption budgets to safely drain underutilized nodes, and configure aggressive Cluster Autoscaler scale-down settings (5-10 minute unneeded time threshold instead of the default 10 minutes). Use a cost visibility tool like Kubecost to identify zombie deployments—I routinely find dev/staging workloads left running 24/7 that should only spin up during business hours.

The three main sources of idle waste:

Overprovisioned pods: Teams request 4 CPU cores “just in case,” then use 0.6 cores at p95. This is fear-based provisioning, and it’s expensive. Use p95 usage patterns, add 15-20% headroom, and remove CPU limits where safe (they cause throttling even when the node has spare capacity).

Stranded node capacity: When pods don’t bin-pack efficiently, you end up with nodes running at 40% utilization because the next pod won’t fit. This is where Karpenter shines on EKS—it provisions the exact instance type needed for pending pods instead of using fixed node groups.

Unused environments: Staging and dev clusters left running nights and weekends. Implement cluster hibernation for non-prod environments—GKE Autopilot makes this trivial, or use tools like Downscaler for EKS/AKS to scale deployments to zero replicas outside business hours.

Real-world savings: Staging namespace scheduling
The $40K/year savings paid for a dedicated cost optimization engineer.

What Are the Best Kubernetes Cost Tools in 2026?

For autonomous optimization that fixes issues automatically, use PerfectScale ($0.50/node/month); for comprehensive cost visibility and alerts, use Kubecost (free tier covers basics); for infrastructure automation on AWS, use CAST AI; and for cross-cloud cost intelligence, use CloudZero. Most teams start with Kubecost’s free tier to identify problems, then layer on automation tools for high-spend workloads.

Here’s what each excels at:

PerfectScale: Continuously rightsizes workloads in production without waiting for human approval. It’s the only tool I’ve seen that combines resource optimization with actual risk assessment—won’t shrink a database pod during peak traffic. Pricing starts at $0.50/node/month, pays for itself in the first week for clusters over 20 nodes.

Kubecost: The gold standard for visibility. Shows cost per namespace, label, deployment—basically any K8s dimension. The free tier is surprisingly capable (supports unlimited nodes now). Upgrade to Enterprise ($500+/month) if you need multi-cluster cost allocation or showback/chargeback for internal teams. Pairs well with Grafana for alerting.

CAST AI: Automates instance type selection, spot instance management, and cluster rightssizing for AWS, GCP, and Azure. Best feature is automatic spot fallback—if spot gets interrupted, it transparently moves pods to on-demand without you noticing. Free tier for single cluster, paid tiers start at $300/month.

Karpenter (AWS EKS only): Not technically a cost tool, but the fastest way to eliminate node-level waste on EKS. Provisions nodes in ~60 seconds vs 3-5 minutes for Cluster Autoscaler. Open source, zero cost beyond AWS infrastructure charges.

CloudZero: Enterprise-grade cost intelligence across your entire stack (not just K8s). Shows how much your “payments” microservice actually costs including RDS, S3, networking—not just the pod compute. Overkill for small teams, essential at scale. Pricing is custom (expect $2K-10K/month depending on cloud spend).

I see teams implement five different tools that all show slightly different numbers, then spend weeks reconciling them instead of actually optimizing. There’s no magic “set it and forget it” button for K8s costs—you either monitor actively or you burn money passively. Start with Kubecost to baseline your spend, identify your top 3 cost drivers, then pick one automation tool to fix them.

How Do I Use Spot Instances for Kubernetes?

Use spot instances for stateless workloads that tolerate interruptions—batch jobs, CI/CD runners, web frontends—not for databases or StatefulSets. Configure node affinity and taints to keep critical pods on on-demand nodes, then use Karpenter or CAST AI to automatically manage spot capacity with fallback to on-demand when spot isn’t available.

Spot instances offer 70-90% discounts compared to on-demand pricing, but they can be interrupted with 2 minutes notice when AWS/GCP/Azure needs the capacity back. The trick is architectural—design for interruption, don’t fight it.

For EKS with Karpenter, configure a provisioner that mixes spot and on-demand:

apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
  name: default
spec:
  requirements:
    - key: karpenter.sh/capacity-type
      operator: In
      values: ["spot", "on-demand"]
  limits:
    resources:
      cpu: 1000

This lets Karpenter use spot when available, fall back to on-demand when it’s not. Set pod-level node selectors for critical workloads:

nodeSelector:
  karpenter.sh/capacity-type: on-demand

I’ve run production EKS clusters at 70% spot capacity (30% on-demand for databases and critical services). Total compute savings was 48% compared to all on-demand, with zero customer-impacting incidents over 18 months.

The gotcha: cross-zone traffic costs add up fast if your pods restart in different availability zones. For chatty services (high pod-to-pod traffic), pin them to single AZs using topology spread constraints. Saved a client $3,800/month in data transfer by doing this.

FAQ

What percentage of Kubernetes costs can be optimized?

Most Kubernetes clusters can reduce infrastructure costs by 40-60% through right-sizing, autoscaling, and spot instances without impacting application performance. The quick wins come from removing overprovisioned resources (typically 20-30% savings), implementing cluster autoscaling to eliminate idle nodes (10-15% savings), and moving fault-tolerant workloads to spot instances (15-25% additional savings). I’ve seen teams hit 60%+ savings when they also negotiate reserved instance commitments for predictable baseline capacity.

Should I use Horizontal Pod Autoscaler or Vertical Pod Autoscaler?

Use Horizontal Pod Autoscaler (HPA) for production workloads—it scales the number of pod replicas based on demand without restarting pods. Vertical Pod Autoscaler (VPA) adjusts CPU/memory requests but requires pod restarts, making it unsuitable for production apps with high availability requirements. VPA works well for batch jobs or workloads where brief interruptions don’t matter. Never run HPA and VPA on the same deployment—they conflict and create scaling loops.

Is Karpenter better than Cluster Autoscaler for AWS EKS?

Yes, Karpenter is significantly better for EKS—it provisions nodes 3-5x faster (60 seconds vs 3-5 minutes), supports more flexible instance type selection, and bins pods more efficiently to reduce stranded capacity. Cluster Autoscaler works fine for small clusters under 50 nodes, but Karpenter’s ability to provision exactly the right instance type for pending pods eliminates waste from fixed node groups. If you’re on EKS 1.28 or later, migrate to Karpenter—it’s become the AWS-recommended solution and gets better support.

How often should I review Kubernetes resource requests?

Review resource requests for high-spend workloads monthly, and set up automated alerts when actual usage diverges 30%+ from requested resources for more than 7 days. Use tools like Kubecost or PerfectScale to continuously monitor the gap between requested and actual usage. Your top 10 deployments typically represent 60-70% of cluster cost—those deserve weekly attention. Lower-priority workloads can be reviewed quarterly. The goal isn’t constant optimization, it’s catching the big misconfigurations quickly before they cost thousands.

What’s the best way to reduce cross-zone traffic costs in Kubernetes?

Use topology spread constraints to keep pods for the same service in a single availability zone, reducing cross-AZ data transfer charges that run $0.01-0.02/GB. For stateful workloads and high-traffic microservices, configure topologySpreadConstraints with whenUnsatisfiable: DoNotSchedule to prevent pods from spreading unnecessarily. I’ve seen this cut networking costs by 15-25% for chatty applications—one client saved $3,800/month by pinning their message queue consumers to the same AZ as their Kafka brokers. Balance this against high availability needs (you still want cross-AZ redundancy for critical services).

Sources

Pricing Data & Tool Documentation:

• AWS EKS Pricing – January 2026
• Azure AKS Pricing – January 2026
• Google GKE Pricing – January 2026
• PerfectScale Documentation – Pricing and features
• Kubecost Documentation – Free and enterprise tiers
• CAST AI Pricing – Platform tiers
• Karpenter Documentation – AWS EKS autoscaling

Industry Research & Best Practices:

• 13 Best Kubernetes Cost Management Tools in 2026 – nOps
• GKE Cost Optimization: How to Cut Kubernetes Spend at Scale in 2026 – ScaleOps
• Kubernetes cost optimization: 10 proven strategies in 2026 – N-iX
• Top 18 Kubernetes Cost Optimization Strategies in 2026
• Kubernetes Pricing 2026: EKS vs AKS vs GKE Comparison Guide | Sedai
• AWS EKS vs Azure AKS vs Google GKE: Complete Comparison Guide for 2026 | Tasrie IT Services
• Autoscaling Workloads | Kubernetes
• Kubernetes Autoscaling in 2025: Best Practices, Tools, and Optimization Strategies
• Guide to Kubernetes Scaling: Horizontal, Vertical & Cluster